"""Pytorch impl of MxNet Gluon ResNet/(SE)ResNeXt variants
This file evolved from https://github.com/pytorch/vision 'resnet.py' with (SE)-ResNeXt additions
and ports of Gluon variations (https://github.com/dmlc/gluon-cv/blob/master/gluoncv/model_zoo/resnet.py) 
by Ross Wightman
"""

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg
from .layers import SEModule
from .registry import register_model
from .resnet import ResNet, Bottleneck, BasicBlock


def _cfg(url="", **kwargs):
    return {
        "url": url,
        "num_classes": 1000,
        "input_size": (3, 224, 224),
        "pool_size": (7, 7),
        "crop_pct": 0.875,
        "interpolation": "bicubic",
        "mean": IMAGENET_DEFAULT_MEAN,
        "std": IMAGENET_DEFAULT_STD,
        "first_conv": "conv1",
        "classifier": "fc",
        **kwargs,
    }


default_cfgs = {
    "gluon_resnet18_v1b": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet18_v1b-0757602b.pth"
    ),
    "gluon_resnet34_v1b": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet34_v1b-c6d82d59.pth"
    ),
    "gluon_resnet50_v1b": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1b-0ebe02e2.pth"
    ),
    "gluon_resnet101_v1b": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1b-3b017079.pth"
    ),
    "gluon_resnet152_v1b": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1b-c1edb0dd.pth"
    ),
    "gluon_resnet50_v1c": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1c-48092f55.pth",
        first_conv="conv1.0",
    ),
    "gluon_resnet101_v1c": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1c-1f26822a.pth",
        first_conv="conv1.0",
    ),
    "gluon_resnet152_v1c": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1c-a3bb0b98.pth",
        first_conv="conv1.0",
    ),
    "gluon_resnet50_v1d": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth",
        first_conv="conv1.0",
    ),
    "gluon_resnet101_v1d": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.pth",
        first_conv="conv1.0",
    ),
    "gluon_resnet152_v1d": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.pth",
        first_conv="conv1.0",
    ),
    "gluon_resnet50_v1s": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth",
        first_conv="conv1.0",
    ),
    "gluon_resnet101_v1s": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.pth",
        first_conv="conv1.0",
    ),
    "gluon_resnet152_v1s": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.pth",
        first_conv="conv1.0",
    ),
    "gluon_resnext50_32x4d": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext50_32x4d-e6a097c1.pth"
    ),
    "gluon_resnext101_32x4d": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_32x4d-b253c8c4.pth"
    ),
    "gluon_resnext101_64x4d": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_64x4d-f9a8e184.pth"
    ),
    "gluon_seresnext50_32x4d": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext50_32x4d-90cf2d6e.pth"
    ),
    "gluon_seresnext101_32x4d": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_32x4d-cf52900d.pth"
    ),
    "gluon_seresnext101_64x4d": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_64x4d-f9926f93.pth"
    ),
    "gluon_senet154": _cfg(
        url="https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_senet154-70a1a3c0.pth",
        first_conv="conv1.0",
    ),
}


def _create_resnet(variant, pretrained=False, **kwargs):
    return build_model_with_cfg(
        ResNet, variant, pretrained, default_cfg=default_cfgs[variant], **kwargs
    )


@register_model
def gluon_resnet18_v1b(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model."""
    model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], **kwargs)
    return _create_resnet("gluon_resnet18_v1b", pretrained, **model_args)


@register_model
def gluon_resnet34_v1b(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model."""
    model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], **kwargs)
    return _create_resnet("gluon_resnet34_v1b", pretrained, **model_args)


@register_model
def gluon_resnet50_v1b(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model."""
    model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], **kwargs)
    return _create_resnet("gluon_resnet50_v1b", pretrained, **model_args)


@register_model
def gluon_resnet101_v1b(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model."""
    model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], **kwargs)
    return _create_resnet("gluon_resnet101_v1b", pretrained, **model_args)


@register_model
def gluon_resnet152_v1b(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model."""
    model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], **kwargs)
    return _create_resnet("gluon_resnet152_v1b", pretrained, **model_args)


@register_model
def gluon_resnet50_v1c(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model."""
    model_args = dict(
        block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type="deep", **kwargs
    )
    return _create_resnet("gluon_resnet50_v1c", pretrained, **model_args)


@register_model
def gluon_resnet101_v1c(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model."""
    model_args = dict(
        block=Bottleneck,
        layers=[3, 4, 23, 3],
        stem_width=32,
        stem_type="deep",
        **kwargs
    )
    return _create_resnet("gluon_resnet101_v1c", pretrained, **model_args)


@register_model
def gluon_resnet152_v1c(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model."""
    model_args = dict(
        block=Bottleneck,
        layers=[3, 8, 36, 3],
        stem_width=32,
        stem_type="deep",
        **kwargs
    )
    return _create_resnet("gluon_resnet152_v1c", pretrained, **model_args)


@register_model
def gluon_resnet50_v1d(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model."""
    model_args = dict(
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        stem_width=32,
        stem_type="deep",
        avg_down=True,
        **kwargs
    )
    return _create_resnet("gluon_resnet50_v1d", pretrained, **model_args)


@register_model
def gluon_resnet101_v1d(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model."""
    model_args = dict(
        block=Bottleneck,
        layers=[3, 4, 23, 3],
        stem_width=32,
        stem_type="deep",
        avg_down=True,
        **kwargs
    )
    return _create_resnet("gluon_resnet101_v1d", pretrained, **model_args)


@register_model
def gluon_resnet152_v1d(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model."""
    model_args = dict(
        block=Bottleneck,
        layers=[3, 8, 36, 3],
        stem_width=32,
        stem_type="deep",
        avg_down=True,
        **kwargs
    )
    return _create_resnet("gluon_resnet152_v1d", pretrained, **model_args)


@register_model
def gluon_resnet50_v1s(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model."""
    model_args = dict(
        block=Bottleneck, layers=[3, 4, 6, 3], stem_width=64, stem_type="deep", **kwargs
    )
    return _create_resnet("gluon_resnet50_v1s", pretrained, **model_args)


@register_model
def gluon_resnet101_v1s(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model."""
    model_args = dict(
        block=Bottleneck,
        layers=[3, 4, 23, 3],
        stem_width=64,
        stem_type="deep",
        **kwargs
    )
    return _create_resnet("gluon_resnet101_v1s", pretrained, **model_args)


@register_model
def gluon_resnet152_v1s(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model."""
    model_args = dict(
        block=Bottleneck,
        layers=[3, 8, 36, 3],
        stem_width=64,
        stem_type="deep",
        **kwargs
    )
    return _create_resnet("gluon_resnet152_v1s", pretrained, **model_args)


@register_model
def gluon_resnext50_32x4d(pretrained=False, **kwargs):
    """Constructs a ResNeXt50-32x4d model."""
    model_args = dict(
        block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, **kwargs
    )
    return _create_resnet("gluon_resnext50_32x4d", pretrained, **model_args)


@register_model
def gluon_resnext101_32x4d(pretrained=False, **kwargs):
    """Constructs a ResNeXt-101 model."""
    model_args = dict(
        block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4, **kwargs
    )
    return _create_resnet("gluon_resnext101_32x4d", pretrained, **model_args)


@register_model
def gluon_resnext101_64x4d(pretrained=False, **kwargs):
    """Constructs a ResNeXt-101 model."""
    model_args = dict(
        block=Bottleneck, layers=[3, 4, 23, 3], cardinality=64, base_width=4, **kwargs
    )
    return _create_resnet("gluon_resnext101_64x4d", pretrained, **model_args)


@register_model
def gluon_seresnext50_32x4d(pretrained=False, **kwargs):
    """Constructs a SEResNeXt50-32x4d model."""
    model_args = dict(
        block=Bottleneck,
        layers=[3, 4, 6, 3],
        cardinality=32,
        base_width=4,
        block_args=dict(attn_layer=SEModule),
        **kwargs
    )
    return _create_resnet("gluon_seresnext50_32x4d", pretrained, **model_args)


@register_model
def gluon_seresnext101_32x4d(pretrained=False, **kwargs):
    """Constructs a SEResNeXt-101-32x4d model."""
    model_args = dict(
        block=Bottleneck,
        layers=[3, 4, 23, 3],
        cardinality=32,
        base_width=4,
        block_args=dict(attn_layer=SEModule),
        **kwargs
    )
    return _create_resnet("gluon_seresnext101_32x4d", pretrained, **model_args)


@register_model
def gluon_seresnext101_64x4d(pretrained=False, **kwargs):
    """Constructs a SEResNeXt-101-64x4d model."""
    model_args = dict(
        block=Bottleneck,
        layers=[3, 4, 23, 3],
        cardinality=64,
        base_width=4,
        block_args=dict(attn_layer=SEModule),
        **kwargs
    )
    return _create_resnet("gluon_seresnext101_64x4d", pretrained, **model_args)


@register_model
def gluon_senet154(pretrained=False, **kwargs):
    """Constructs an SENet-154 model."""
    model_args = dict(
        block=Bottleneck,
        layers=[3, 8, 36, 3],
        cardinality=64,
        base_width=4,
        stem_type="deep",
        down_kernel_size=3,
        block_reduce_first=2,
        block_args=dict(attn_layer=SEModule),
        **kwargs
    )
    return _create_resnet("gluon_senet154", pretrained, **model_args)
